Initial genotype filtering

Our first step (after conversion of the joint called .vcf file to a hail matrix table) is to remove genotypes based on the following collection of criteria:

• If homozygous reference, remove if at least one of the following is true:
  • Genotype quality \(<\) 20
  • Depth \(<\) 10
• If heterozygous, at least one of the following is true:
  • (Reference allele depth + alternative allele depth) divided by total depth \(<\) 0.8
  • Alternative allele depth divided by total depth \(<\) 0.2
  • Reference phred-scaled genotype posterior \(<\) 20
  • Depth \(<\) 10
• If homozygous variant, at least one of the following is true:
  • Alternative allele depth divided by total depth \(<\) 0.2
  • Reference phred-scaled genotype posterior \(<\) 20
  • Depth \(<\) 10

Initial variant filtering

Remove variants that either:

• Fall in a low complexity region
• Fail VQSR
• Fall outside padded target intervals (50bp padding)
• Are invariant after the initial GT filter

Initial sample quality control

We run the sample_qc function in hail and remove samples according to the following:

• Sample call rate \(<\) 0.93
• FREEMIX contamination (%) \(>\) 0.02
• Percentage chimeras (%) \(>\) 0.015
• Mean depth \(<\) 30
• Mean genotype quality \(<\) 55

Thresholds used were based on plotting the distributions of these metrics. A full collection of plots can be found in the repository. Here we show boxplots with overlaid scatterplots of the above metrics, split by sequencing batch, and coloured by location. The threshold for exclusion is shown as a dashed line.

Following this step, we export high quality variants (allele frequency between 0.01 to 0.99 with high call rate (> 0.98)) to plink format and prune to pseudo-independent SNPs using --indep 50 5 2. This pruned set of SNPs feeds into the next few stages of the QC pipeline.

Sex imputation

We impute the sexes of the individuals with this pruned set of variants on the X chromosome, and create list of samples with incorrect or unknown sex as defined by:

• Sex is unknown in the phenotype files
• F-statistic \(>\) 0.6 and the sex is female in the phenotype file
• F-statistic \(<\) 0.6 and the sex is male in the phenotype file

Here we show the distribution of the F-statistic, with the 0.6 threshold defining our sex impututation shown as a dashed line.

IBD

Using the identity_by_descent method in hail, we evaluate \(\hat{\pi}\) between pairs of samples, and filter based on a threshold of 0.2 shown as a dashed line on the plot below.

We then create a sample list of patients such that no pair has \(\hat{\pi} >\) 0.2.

PCA

We next perform a number of principal component analysis (PCA) steps to ensure that we have matched cases and controls in our cleaned dataset.

Final variant filtering

For our final variant filtering step, we first restrict to samples in the strictly defined European subset, filter to the unrelated list, and filter out samples with incorrectly defined sex or unknown sex, and run variant QC. We then evaluate a collection of variant metrics and remove variants that satisfy at least one of:

• Invariant site in cleaned sample subset
• Call rate \(<\) 0.97
• Control call rate \(<\) 0.97
• Case call rate \(<\) 0.97
• \(|\)Case call rate - Control call rate\(| >\) 0.02
• \(p\)-value for Hardy Weinberg Equilibrium \(<\) 10^-6

The following plots show the 0.97 threshold for call rate and 0.02 threshold for difference in call rate between cases and controls respectively.

After these steps we plot the resulting changes in metrics across the samples in our data set. Each of the following plots splits the data by sequencing data and colours the points based on location. The first collection of subplots in each figure shows the variant metrics before sample removal, with the lower collection of subplots showing the resultant change after our QC steps.

Final sample filtering

In this step we remove sample outliers after the variant cleaning in the previous step. Samples are removed if at least one of the following lies more that three standard deviations away from the mean:

• Ratio of transitions to transversions
• Ratio of heterozygous to homozygous variant
• Ratio of insertions to deletions
• Number of singletons

As a final step, we export common (allele frequency between 0.01 and 0.99) variants to plink format, prune, and evaluate final principal components for downstream analysis. The first six principal components are displayed below and coloured by case status.

After all of this data cleaning, we save the resultant hail matrix tables for downstream analyses.

The resultant composition of the samples was as follows:

The bipolar subtype information of the curated samples is: